Dust storm emission inversion using data assimilation

Abstract

Severe dust storms present great threats to the environment, property and human health over the areas in the downwind of arid regions. Several dynamical dust models have been developed to predict the dust concentrations in the atmosphere. Currently, the accuracy of these models is limited mainly due to the imperfect modeling of dust emissions. Along with the progress in the dust and aerosol modeling, the advances in sensor technologies have made large-scale aerosol measurements feasible. The rich measurements provide opportunities to estimate uncertain emission fields, and subsequently, to improve the forecast skill. Such process of emission optimization conditioned on measurements is usually referred as emission inversion. Here, the termof emission inversion specially represents the way of deriving estimates from observations through the use of an atmospheric chemical transport model and a data assimilationmethod.